Articles | Volume 17, issue 4
Research article
22 Feb 2024
Research article |  | 22 Feb 2024

A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization

Anton Rusanen, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-70', Anonymous Referee #1, 26 May 2023
    • AC1: 'Reply on RC1', Anton Rusanen, 31 Oct 2023
  • RC2: 'Comment on amt-2023-70', Anonymous Referee #2, 28 Jul 2023
    • AC2: 'Reply on RC2', Anton Rusanen, 31 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Anton Rusanen on behalf of the Authors (23 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (06 Dec 2023) by Eric C. Apel
AR by Anton Rusanen on behalf of the Authors (20 Dec 2023)
Short summary
We present a Bayesian non-negative matrix factorization model that performs better on our test datasets than currently widely used models. Its advantages are better use of time information and providing a direct error estimation. We believe this could lead to better estimates of emission sources from measurements.